Add ElementWise support (pytorch#150)

Author: zheng-xq

benchmarks/tensorexpr/elementwise.py

@@ -7,13 +7,16 @@ def __init__(self, mode, device, N):
         self.N = N
         self.d1 = self.rand([N], device=device, requires_grad=self.requires_grad)
         self.d2 = self.rand([N], device=device, requires_grad=self.requires_grad)
+        self.d3 = self.rand([N], device=device, requires_grad=self.requires_grad)
+        self.d4 = self.rand([N], device=device, requires_grad=self.requires_grad)
+        self.inputs = [self.d1, self.d2, self.d3, self.d4]
 
-    def forward(self):
-        y = self.mul(self.d1, self.d2)
+    def forward(self, d1, d2, d3, d4):
+        y = d1 * d2 + d3 * d4
         return y
 
     def reference(self):
-        return self.numpy(self.d1) * self.numpy(self.d2)
+        return self.numpy(self.d1) * self.numpy(self.d2) + self.numpy(self.d3) * self.numpy(self.d4)
 
     def config(self):
         return [self.N]
@@ -24,11 +27,11 @@ def module():
 
     def memory_workload(self):
         if self.mode == 'fwd':
-            sol_count = 2 + 1
-            algorithmic_count = 2 + 1
+            sol_count = 4 + 1
+            algorithmic_count = 3 + 1
         else:
-            sol_count = (2 + 1) + (1 + 2)
-            algorithmic_count = (2 + 1) + ((2 + 1) + (2 + 1))
+            sol_count = (4 + 1) + (1 + 4)
+            algorithmic_count = (4 + 1) + ((2 + 1) * 4)
 
         buffer_size = self.N * 4
         return {'sol': buffer_size * sol_count, 'algorithmic': buffer_size * algorithmic_count}